Field of the Invention
The present disclosure generally relates to image processing and, more particularly, to an image processing apparatus, an image processing method, and a recording medium.
Description of the Related Art
Many studies for segmenting an image into a plurality of regions have conventionally been made, and especially in recent years, studies for cutting out semantic regions, such as a human region, a car region, a road region, a building region, and a sky region, from an image have been actively researched. Such an issue is called semantic segmentation, which is considered to be applicable to image correction and scene interpretation adaptive to the types of objects in the image. In semantic segmentation, it has already become commonplace to identify class labels relating to positions of an image not in units of pixels but in units of superpixels. Superpixels are cut out from an image mostly as small regions having similar features. There have been discussed various techniques for cutting out superpixels.
Representative examples include a graph-based technique discussed in non-patent literature 1 (“Efficient Graph-Based Image Segmentation”, P. F. Felzenszwalb, International Journal of Computer Vision (IJCV), 2004) and a clustering-based technique discussed in non-patent literature 2 (“SLIC Superpixels”, R. Achanta, A. Shaji, K. Smith, A. Lucchi, EPFL Technical Report, 2010). Superpixels thus obtained are subjected to the identification of class labels by using feature amounts inside the superpixels. Context feature amounts nearby may be used as well. Various training images are usually used to train such local-based region identifiers for identifying regions.
When identifying a region class on an image by using a region identifier, superpixels of the same class category may have different image features depending on the imaging situation. For example, a cloud may be captured in white during the daytime while the same cloud, if captured with the setting sun, can be in orange due to the reflection of the sun light. In such a case, the orange cloud in the sunset image and a textureful orange wall captured during the daytime are similar on a feature space. If the sunset image and the image of the orange wall are both learned by the region identifiers by using various training images as described above, it is difficult to distinguish these images.
Japanese Patent No. 4,942,510 discusses a technique for recognizing a vehicle adaptively to vehicle angles and weather variations by subdividing the problem. According to the technique, support vector machines (SVMs) corresponding to respective conditions are prepared depending on the numbers of horizontal lines and vertical lines in an object region and contrast. Vehicle recognition is performed by switching the SVMs according to the condition. In such an example, the recognition problems are simplified by switching the problems at predetermined thresholds of the foregoing condition.
The method discussed in Japanese Patent No. 4,942,510 is based on a concept called divide and rule, which includes dividing a problem based on a change in a situation and switching solutions. However, when dividing a problem based on conditions, it is not necessarily the best approach for a human to deliberately determine the condition. For example, in the case of distinguishing a daytime scene and an evening scene, the boundary between the daytime and evening is obscure and not clearly definable. Other than the daytime and evening, there may also be other situations in which problems can be divided for simplification, but such situations are difficult to know in advance.